PrerequisiteInput

class lsst.pipe.base.connectionTypes.PrerequisiteInput(name: str, storageClass: str, doc: str = '', multiple: bool = False, dimensions: Iterable[str] = (), isCalibration: bool = False, deferLoad: bool = False, lookupFunction: Optional[Callable[[lsst.daf.butler.DatasetType, lsst.daf.butler.registry.Registry, lsst.daf.butler.DataCoordinate, lsst.daf.butler.registry.wildcards.CollectionSearch], Iterable[lsst.daf.butler.DatasetRef]]] = None)

Bases: lsst.pipe.base.connectionTypes.BaseInput

Class used for declaring PipelineTask prerequisite connections

Parameters

namestr

The default name used to identify the dataset type

storageClassstr

The storage class used when (un)/persisting the dataset type

multiplebool

Indicates if this connection should expect to contain multiple objects of the given dataset type

dimensionsiterable of str

The lsst.daf.butler.Butler lsst.daf.butler.Registry dimensions used to identify the dataset type identified by the specified name

deferLoadbool

Indicates that this dataset type will be loaded as a lsst.daf.butler.DeferredDatasetHandle. PipelineTasks can use this object to load the object at a later time.

lookupFunction: `typing.Callable`, optional

An optional callable function that will look up PrerequisiteInputs using the DatasetType, registry, quantum dataId, and input collections passed to it. If no function is specified, the default temporal spatial lookup will be used.

Notes

Prerequisite inputs are used for datasets that must exist in the data repository before a pipeline including this is run; they cannot be produced by another task in the same pipeline.

In exchange for this limitation, they have a number of advantages relative to regular Input connections:

• The query used to find them then during QuantumGraph generation can be fully customized by providing a lookupFunction.

• Failed searches for prerequisites during QuantumGraph generation will usually generate more helpful diagnostics than those for regular Input connections.

• The default query for prerequisite inputs relates the quantum dimensions directly to the dimensions of its dataset type, without being constrained by any of the other dimensions in the pipeline. This allows them to be used for temporal calibration lookups (which regular Input connections cannot do at present) and to work around QuantumGraph generation limitations involving cases where naive spatial overlap relationships between dimensions are not desired (e.g. a task that wants all detectors in each visit for which the visit overlaps a tract, not just those where that detector+visit combination overlaps the tract).

Attributes Summary

deferLoad
dimensions
doc
isCalibration
lookupFunction
multiple

Methods Summary

makeDatasetType(universe[, parentStorageClass])

Construct a true DatasetType instance with normalized dimensions.

Attributes Documentation

deferLoad: bool = False

dimensions: Iterable[str] = ()

doc: str = ''

isCalibration: bool = False

lookupFunction: Optional[Callable[[lsst.daf.butler.DatasetType, lsst.daf.butler.registry.Registry, lsst.daf.butler.DataCoordinate, lsst.daf.butler.registry.wildcards.CollectionSearch], Iterable[lsst.daf.butler.DatasetRef]]] = None

multiple: bool = False

Methods Documentation

makeDatasetType(universe: lsst.daf.butler.DimensionUniverse, parentStorageClass: Optional[lsst.daf.butler.StorageClass] = None)

Construct a true DatasetType instance with normalized dimensions.

Parameters

universelsst.daf.butler.DimensionUniverse

Set of all known dimensions to be used to normalize the dimension names specified in config.

parentStorageClasslsst.daf.butler.StorageClass, optional

Parent storage class for component datasets; None otherwise.

Returns

datasetTypeDatasetType

The DatasetType defined by this connection.